Objective evaluation of Parkinson's disease bradykinesia

Bradykinesia is the fundamental motor feature of Parkinson’s disease - obligatory for diagnosis and central to monitoring. It is a complex clinicalsign that describes movements with slow speed, small amplitude, irregular rhythm, brief pauses and progressive decrements. Clinical ascertainment of the presence and severity of bradykinesia relies on subjective interpretation of these components, with considerable variability amongst clinicians, and this may contribute to diagnostic error and inaccurate monitoring in Parkinson’s disease. The primary aim of this thesis was to assess whether a novel non-invasive device could objectively measure bradykinesia and predict diagnostic classification of movement data from Parkinson’s disease patients and healthy controls. The second aim was to evaluate how objective measures of bradykinesia correlate with clinical measures of bradykinesia severity. The third aim was to investigate the characteristic kinematic features of bradykinesia. Forty-nine patients with Parkinson’s disease and 41 healthy controls were recruited in Leeds. They performed a repetitive finger-tapping task for 30 seconds whilst wearing small electromagnetic tracking sensors on their finger and thumb. Movement data was analysed using two different methods - statistical measures of the separable components of bradykinesia and a computer science technique called evolutionary algorithms. Validation data collected independently from 13 patients and nine healthy controls in San Francisco was used to assess whether the results generalised. The evolutionary algorithm technique was slightly superior at classifying the movement data into the correct diagnostic groups, especially for the mildest clinical grades of bradykinesia, and they generalised to the independent group data. The objective measures of finger tapping correlated well with clinical grades of bradykinesia severity. Detailed analysis of the data suggests that a defining feature of Parkinson’s disease bradykinesia called the sequence effect may be a physiological rather than a pathological phenomenon. The results inform the development of a device that may support clinical diagnosis and monitoring of Parkinson’s disease and also be used to investigate bradykinesia

Using Epigenetic Networks for the Analysis of Movement Associated with Levodopa Therapy for Parkinson's Disease

© 2016 The Author(s) Levodopa is a drug that is commonly used to treat movement disorders associated with Parkinson's disease. Its dosage requires careful monitoring, since the required amount changes over time, and excess dosage can lead to muscle spasms known as levodopa-induced dyskinesia. In this work, we investigate the potential for using epiNet, a novel artificial gene regulatory network, as a classifier for monitoring accelerometry time series data collected from patients undergoing levodopa therapy. We also consider how dynamical analysis of epiNet classifiers and their transitions between different states can highlight clinically useful information which is not available through more conventional data mining techniques. The results show that epiNet is capable of discriminating between different movement patterns which are indicative of either insufficient or excessive levodopa

Evaluating Digital Health Technologies to Advance Parkinson's Disease Care

Parkinson’s disease (PD) is a common progressive neurological disorder characterised by a complex range of motor and non-motor symptoms (NMS). Current PD service provision does not meet the needs of patients, and puts pressure on services with limited capacity. Digital Health Technologies (DHTs), including body-worn sensors and portable devices, may provide advantages, by enabling continual and objective monitoring of symptoms, and facilitating patient self-management. I carried out a series of studies and evaluations of DHTs for use in PD, to evaluate their ability to identify and monitor symptoms in both a clinical and research context. These included: 1. The evaluation of a computerised paced finger tapping task (PFT) that was found to correlate with a measure of verbal fluency, suggesting there may be potential to implement the PFT as part of a wider finger tapping battery to be used as a screening tool for PD executive dysfunction. 2. The iterative, user-centred design and formative evaluation of NMS Assist, a smartphone-based app to enable regular assessment of NMS as well as provide education for patients. The app was found to be highly usable, and key areas of amendment were identified. 3. A clinical service evaluation of the PKGTM, a PD remote monitoring device. The findings revealed the PKGTM is useful for identifying patients with unmet treatment need, even in newly diagnosed people with Parkinson’s (PwP) who experience more frequent clinic review. 4. A systematic review of neuroprotective trial design in PD. The results demonstrated a wide range of primary outcome measures is used across trials, and there is little evidence of patient stratification. The findings highlighted the potential for DHTs to improve various aspects of clinical trial design. I discuss the potential value of DHTs, as well as challenges associated with their use, identified as a result of this research

Characterization of neurological disorders using evolutionary algorithms

The life expectancy increasing, in the last few decades, leads to a large diffusion of neurodegenerative age-related diseases such as Parkinson’s disease. Neurodegenerative diseases are part of the huge category of neurological disorders, which comprises all the disorders affecting the central nervous system. These conditions have a terrible impact on life quality of both patients and their families, but also on the costs associated to the society for their diagnosis and management. In order to reduce their impact on individuals and society, new better strategies for the diagnosis and monitoring of neurological disorders need to be considered. The main aim of this study is investigating the use of artificial intelligence techniques as a tool to help the doctors in the diagnosis and the monitoring of two specific neurological disorders (Parkinson’s disease and dystonia), for which no objective clinical assessments exist. The evolutionary algorithms are chosen as the artificial intelligence technique to evolve the best classifiers. The classifiers evolved by the chosen technique are then compared with those evolved by two popular well-known techniques: artificial neural network and support vector machine. All the evolved classifiers are not only able to distinguish among patients and healthy subjects but also among different subgroups of patients. For Parkinson’s disease: two different cognitive impairment subgroups of patients are considered, with the aim of an early diagnosis and a better monitoring. For dystonia: two kinds of dystonia patients are considered (organic and functional) to have a better insight in the division of the two groups. The results obtained for Parkinson’s disease are encouraging and evidenced some differences among the cognitive impairment subgroups. Dystonia results are not satisfactory at this stage, but the study presents some limitations that could be overcome in future work

The role of the pedunculopontine region in basal ganglia mechanisms of akinesia

There has in recent years been a resurgence of interest in the treatment of Parkinson's disease by stereotactic surgical techniques. The phenomenal success of levo-dopa in the 1960s led to the virtual disappearance of surgery for Parkinson's Disease. However, two decades after its introduction the problems associated with the long-term administration of levo-dopa became well recognized. Over the same period the advance in our knowledge of the neural mechanisms underlying parkinsonian symptoms has been remarkable. Studies established that the loss of the nigro-striatal dopaminergic projection results in overactivity of the GPm and SNr inhibitory output, which in turn depresses the motor activity of thalamic and brainstem structures to which they have been shown to project, thus leading to the clinical manifestations of parkinsonism. It has long been assumed that the increased inhibitory output of the GPm acts via the thalamocortical feedback route to produce akinesia. However, this view fails to explain the clinical and experimental observation that thalamotomy, despite relieving tremor, rarely improves akinesia. Conversely medial pallidotomy may alleviate akinesia but has a lesser effect upon tremor, whereas high frequency stimulation or lesioning of the STN improves both symptoms. As thalamic lesioning does not affect the descending outputs of the basal ganglia, whereas pallidotomy and subthalamic nucleotomy do, a logical conclusion would be that overactivity of descending projections to the pedunculopontine area in the upper brainstem, rather than the overinhibition of the thalamic motor nuclei, is responsible for the akinesia of Parkinson's Disease. Therefore I have studied the effects of lesions of the PPN on movements in monkeys. The results establish that in the normal monkey a unilateral lesion of the PPN will result in a temporary akinetic state, whereas bilateral PPN lesions will generate a lasting Parkinsonian like akinesia. Results are consistent whether the lesioning method is by radiofrequency thermocoagulation, or by pressure injection of a neuron specific excitotoxic agent. Clinically, I have worked with the Oxford Movement disorder group studying the effects of lesioning, and deep brain stimulation in the basal ganglia of Parkinsonian patients

Classification of Resting-State fMRI using Evolutionary Algorithms: Towards a Brain Imaging Biomarker for Parkinson’s Disease

It is commonly accepted that accurate early diagnosis and monitoring of neurodegenerative conditions is essential for effective disease management and delivery of medication and treatment. This research develops automatic methods for detecting brain imaging preclinical biomarkers for Parkinson’s disease (PD) by considering the novel application of evolutionary algorithms. An additional novel element of this work is the use of evolutionary algorithms to both map and predict the functional connectivity in patients using rs-fMRI data. Specifically, Cartesian Genetic Programming was used to classify dynamic causal modelling data as well as timeseries data. The findings were validated using two other commonly used classification methods (Artificial Neural Networks and Support Vector Machines) and by employing k-fold cross-validation. Across dynamic causal modelling and timeseries analyses, findings revealed maximum accuracies of 75.21% for early stage (prodromal) PD patients in which patients reveal no motor symptoms versus healthy controls, 85.87% for PD patients versus prodromal PD patients, and 92.09% for PD patients versus healthy controls. Prodromal PD patients were classified from healthy controls with high accuracy – this is notable and represents the key finding since current methods of diagnosing prodromal PD have low reliability and low accuracy. Furthermore, Cartesian Genetic Programming provided comparable performance accuracy relative to Artificial Neural Networks and Support Vector Machines. Nevertheless, evolutionary algorithms enable us to decode the classifier in terms of understanding the data inputs that are used, more easily than in Artificial Neural Networks and Support Vector Machines. Hence, these findings underscore the relevance of both dynamic causal modelling analyses for classification and Cartesian Genetic Programming as a novel classification tool for brain imaging data with medical implications for disease diagnosis, particularly in early stages 5-20 years prior to motor symptoms

Compulsive use of dopaminergic drugs in Parkinson's disease.

A small group of patients with Parkinson's disease (PD) compulsively use dopaminergic medications despite the frequent emergence of harmful physical, psychiatric and social effects. This behavioural syndrome has been termed the dopamine dysregulation syndrome (DDS) and although closely related, should be distinguished from impulse control disorders. The phenomenology, risk factors and neurobiology of DDS have been explored in a series of observational, neuropsychological and pharmacological clinical studies. Dopaminergic drug-responsive complex repetitive stereotypical behaviours (punding) were identified and characterised in PD outpatients selected on the basis of their dopaminergic drug intake. In animal models of Parkinson's disease, stereotypies are known to index the neuroadaptive changes of sensitisation. Punding was found to be associated with DDS, dyskinesia severity and harmful neuropsychiatric disturbances raising the possibility that the biological mechanisms underlying drug-reward and these behaviours may overlap. Psychostimulant drugs have powerful effects on dopamine release and re-uptake in the presynaptic dopamine system and are capable of inducing neuroplastic changes in the basal ganglia particularly after their intermittent administration. Psychostimulant drugs have dopaminergic effects but have only been demonstrated to have weak anti-Parkinsonian effects. The acute effects of L-dopa and methylphenidate were examined (which has effects similar to psychostimulant drugs) in 15 untreated PD patients, before and again after a mean 18 months of sustained dopaminergic drug therapy. After sustained dopaminergic therapy, the motor effects of L-dopa and the euphoriant effects of methylphenidate were augmented. This provided clinical support in humans for the first time that sustained dopaminergic drug therapy may result in psychomotor sensitisation. In an effort to facilitate early identification of DDS and for planning prompt therapeutic interventions personality traits were examined in PD patients with DDS and compared to those without DDS and healthy controls. DDS patients were found to differ from control PD patients and age-matched healthy controls in personality dimensions linked with substance dependence i.e. high impulsive sensation seeking traits, low harm avoidance, reward dependence, self-directedness and cooperativeness. Impulsive sensation seeking traits in particular, in addition to premorbid addictive behaviour were also found to predict the emergence of DDS suggesting a common neurobiological vulnerability. DDS patients complain of an aversive drug withdrawal state akin to the withdrawal state seen in other forms of addiction. Many patients attribute avoidance of aversive "ofTs" as the reason behind their compulsive drive to self-medicate. This aversive "off'-state was examined in 20 DDS patients and PD controls and found to be associated with behaviours that may lead to sensitisation of brain reward systems. Most authorities believe that compulsive drug-taking and associated behavioural disorders are mediated through the mesolimbic dopaminergic projections and the nucleus accumbens. DDS was investigated using a two-scan nC-Raclopride protocol. Drug-induced sensitisation of ventral striatal- circuitry appeared to mediate compulsive drug "wanting" - providing the first evidence of such in humans. Greater understanding of compulsive dopaminergic drug use in PD should not only inform the management of PD but may provide insight into the mechanisms underlying impulse control disorders

Sistema para análise automatizada de movimento durante a marcha usando uma câmara RGB-D

Nowadays it is still common in clinical practice to assess the gait (or way of walking) of a given subject through the visual observation and use of a rating scale, which is a subjective approach. However, sensors including RGB-D cameras, such as the Microsoft Kinect, can be used to obtain quantitative information that allows performing gait analysis in a more objective way. The quantitative gait analysis results can be very useful for example to support the clinical assessment of patients with diseases that can affect their gait, such as Parkinson’s disease. The main motivation of this thesis was thus to provide support to gait assessment, by allowing to carry out quantitative gait analysis in an automated way. This objective was achieved by using 3-D data, provided by a single RGB-D camera, to automatically select the data corresponding to walking and then detect the gait cycles performed by the subject while walking. For each detected gait cycle, we obtain several gait parameters, which are used together with anthropometric measures to automatically identify the subject being assessed. The automated gait data selection relies on machine learning techniques to recognize three different activities (walking, standing, and marching), as well as two different positions of the subject in relation to the camera (facing the camera and facing away from it). For gait cycle detection, we developed an algorithm that estimates the instants corresponding to given gait events. The subject identification based on gait is enabled by a solution that was also developed by relying on machine learning. The developed solutions were integrated into a system for automated gait analysis, which we found to be a viable alternative to gold standard systems for obtaining several spatiotemporal and some kinematic gait parameters. Furthermore, the system is suitable for use in clinical environments, as well as ambulatory scenarios, since it relies on a single markerless RGB-D camera that is less expensive, more portable, less intrusive and easier to set up, when compared with the gold standard systems (multiple cameras and several markers attached to the subject’s body).Atualmente ainda é comum na prática clínica avaliar a marcha (ou o modo de andar) de uma certa pessoa através da observação visual e utilização de uma escala de classificação, o que é uma abordagem subjetiva. No entanto, existem sensores incluindo câmaras RGB-D, como a Microsoft Kinect, que podem ser usados para obter informação quantitativa que permite realizar a análise da marcha de um modo mais objetivo. Os resultados quantitativos da análise da marcha podem ser muito úteis, por exemplo, para apoiar a avaliação clínica de pessoas com doenças que podem afetar a sua marcha, como a doença de Parkinson. Assim, a principal motivação desta tese foi fornecer apoio à avaliação da marcha, permitindo realizar a análise quantitativa da marcha de forma automatizada. Este objetivo foi atingido usando dados em 3-D, fornecidos por uma única câmara RGB-D, para automaticamente selecionar os dados correspondentes a andar e, em seguida, detetar os ciclos de marcha executados pelo sujeito durante a marcha. Para cada ciclo de marcha identificado, obtemos vários parâmetros de marcha, que são usados em conjunto com medidas antropométricas para identificar automaticamente o sujeito que está a ser avaliado. A seleção automatizada de dados de marcha usa técnicas de aprendizagem máquina para reconhecer três atividades diferentes (andar, estar parado em pé e marchar), bem como duas posições diferentes do sujeito em relação à câmara (de frente para a câmara e de costas para ela). Para a deteção dos ciclos da marcha, desenvolvemos um algoritmo que estima os instantes correspondentes a determinados eventos da marcha. A identificação do sujeito com base na sua marcha é realizada usando uma solução que também foi desenvolvida com base em aprendizagem máquina. As soluções desenvolvidas foram integradas num sistema de análise automatizada de marcha, que demonstrámos ser uma alternativa viável a sistemas padrão de referência para obter vários parâmetros de marcha espácio-temporais e alguns parâmetros angulares. Além disso, o sistema é adequado para uso em ambientes clínicos, bem como em cenários ambulatórios, pois depende de apenas de uma câmara RGB-D que não usa marcadores e é menos dispendiosa, mais portátil, menos intrusiva e mais fácil de configurar, quando comparada com os sistemas padrão de referência (múltiplas câmaras e vários marcadores colocados no corpo do sujeito).Programa Doutoral em Informátic

In vivo Observation of the Release of Norepinephrine and In Vivo Optical Studies on the Direct and Indirect Paths of the Striatum

This thesis focuses on my work using optical techniques to study different brain regions in vivo. The ability to optically study neurons and the circuits they comprise in vivo is an important method to better understand their role in the healthy brain and their dysfunction in disease. The first part of my thesis focuses on my work using on a collaborative project using a new optical probe to study norepinephrine synapses in vivo. In this work we were able to observe the effects of amphetamine on norepinephrine release in vivo and observed some evidence of potential silent synapses. I also describe a new method of cranial window surgery I developed for optical imaging. This technique called PHASOR, is faster, and has a higher success rate, than traditional surgical methods. The improvements demonstrated in this new surgical technique may enable more widespread use of optical imaging methods. In the second part of my thesis, I used optical techniques to study the dorsal striatum in vivo in awake behaving mice. The direct and indirect paths of the dorsal striatum play an important role in motor behavior and motor learning. Dysfunction in these paths has been implicated in motor diseases as well as in mood disorders. In this thesis, I provide a review of the anatomy and physiology of the neurons that comprise the dorsal striatum, and the circuits that they form. The next chapters describe my work using optical techniques to record from these neurons in vivo. In my first set of experiments, I recorded from the direct and indirect paths during a behavioral task of anxiety and observed differential firing depending on the anxiety state of the mouse. Finally, in a preliminary set of experiments, I record from the direct and indirect paths during tasks of motor learning. I found that both paths show changes in firing during motor learning and that these changes differ between the dorsolateral and dorsomedial striatum